Research Associate - Enabling CO2 Storage Using Artificial Intelligence

Role: Research Associate on Enabling CO2 storage using Artificial Intelligence

Grade and Salary: Grade 7, £37,174 - £46,735 per annum

FTE and working pattern: 1FTE, 35hrs per week, Monday – Friday

Contract: Fixed Term for 12 Months

Holiday Entitlement: 33 days annual leave plus 9 buildings closed days (and Christmas Eve when it falls on a weekday)

Purpose of Role

The successful candidate is expected to develop cutting edge deep learning models for multi-scale flow modelling of CO2 in subsurface reservoirs. Two aspects are of special interests (a) pore-to-core scale upscaling (b) upscaling of reactive flow processes at pore-scale. In addition, the successful candidates will contribute to a wide range of AI applications in subsurface flow modelling including (a) stochastic generation of porous media realizations using deep generative models (b) deep learning based property prediction using various architectures (c) Deep learning based proxy modelling with physics based losses and built-in model constrains (e) Efficient coupling of deep learning models to numerical solvers for hybrid CO2 flow modelling. The developed machine learning techniques will be open-sourced and be validated across a wide range of applications and on experimental data and direct numerical simulations generated by the project team.

Key Duties & Responsibilities

The successful candidate will be expected to undertake the following:

• Develop deep learning models for fluid flow in porous media.
• Disseminate research results in peer reviewed journals and interdisciplinary conferences.
• Publish open-source code repositories demonstrating all developed techniques and associated computational notebooks, blogs and presentation materials.
• Participate in regular project meetings with team members and project sponsors.

Essential & Desirable Criteria

Essential

1. A PhD degree in computational science & engineering, applied mathematics, physics or in a related computational field (or close to successful completion).
2. Prior experience in developing deep learning models using open-source libraries.
3. Prior experience in computational fluid dynamics using open-source software packages.
4. Strong track record of publications in high impact scientific journals.
5. Working experience in modern software development techniques (version control, continuous integration, software testing, etc).
6. Excellent verbal and written communication skills, and ability to write professional reports.

How to Apply

Applications can be submitted up to midnight (UK time) on Sunday 01 June 2025.

Please submit your CV & covering letter via the Heriot-Watt on-line recruitment.

We welcome and will consider flexible working patterns e.g., part-time working and job share options.

Heriot-Watt University is committed to securing equality of opportunity in employment and to the creation of an environment in which individuals are selected, trained, promoted, appraised, and otherwise treated on the sole basis of their relevant merits and abilities.  Equality and diversity are all about maximising potential and creating a culture of inclusion for all.

Heriot-Watt University values diversity across our university community and welcomes applications from all sectors of society, particularly from underrepresented groups. For more information, please see our website https://www.hw.ac.uk/uk/services/equality-diversity.htm and also our award-winning work in Disability Inclusive Science Careers https://disc.hw.ac.uk/ .

Use our total rewards calculator: https://www.hw.ac.uk/about/work/total-rewards-calculator.htm to see the value of benefits provided by Heriot-Watt University.

About the Team

The School of Energy, Geoscience, Infrastructure and Society (EGIS) at Heriot-Watt university (HWU), Edinburgh, Scotland has an opening a 12 months PDRA position to work on the ECO-AI project (Enabling CO2 storage using Artificial Intelligence techniques). This post will be based at the Institute of GeoEnergy Engineering (IGE). Further details about ECO-AI project are available at the project webpage https://ai4netzero.github.io/ecoai_project/

About Heriot-Watt University

At Heriot-Watt we are passionate about our values and look to them to connect our people globally and to help us collaborate and celebrate our success through working together. Our research programmes can deliver real world impact which is achieved through the diversity of our international community and the recognition of creative talent that connects our global team.

Our flourishing community will give you the freedom to challenge and to bring your enterprising mind and to help our partners with solutions that can be applied now and in the future. Join us and Heriot Watt will provide you with a platform to thrive and work in a way that also helps you live your life in balance with well-being and inclusiveness at the heart of our global community.